The technical problem at which the present invention is aimed relates to the automatic allocation of a set of sensors, operating in a network, in a manner adapted to the monitoring of a predefined zone of interest. This problem is notably encountered by a monitoring system designer or a user of such a system. It consists, on the basis of a set of sensors each having specific characteristics according to particular technologies, of a set of absolute constraints to be complied with and of a set of properties that it is desired to optimize, in determining the combination, in terms of number and type from among those available, of sensors making it possible to satisfy these absolute constraints and to optimize these properties. A second problem is also aimed at determining the optimum position and optimum adjustment of the sensors determined previously within the monitoring zone so as to optimize performance for constrained resources.
The network of sensors chosen must comply with one or more absolute constraints, for example, a maximum budget, maximum energy reserve, maximum carriage or minimum detection performance.
The properties to be optimized are, for example, the total price of the system, a probability of target detection, an accuracy of location in two or three dimensions. The properties are classed empirically by relative significance.
The technical problem at which the present invention is aimed is a problem of constrained multicriterion optimization of a set of heterogeneous cost functions of arbitrary complexity. These functions being able to be of diverse nature, analytical or non-analytical, continuous or non-continuous, differentiable or non-differentiable. Some constraints may be expressed by simple functions. For example, the total price of the system corresponds to a simple sum of the prices of its constituents. Likewise the total weight of the system is also obtained by the sum of the weights of each sensor. On the other hand, other constraints are modeled by more complex functions. Thus the probability of detecting a target can depend on an a priori probability density of presence of the target, the target-sensor distance or indeed the intervisibility. Accuracy of location based on fusion between sensors can involve the probabilities of detection, the accuracies of elementary measurements and the mutual relative positions of the sensors.
The solutions of the prior art which address the problem of the optimization of sensor networks relate essentially to the deployment of networks of wireless devices and are aimed at optimizing the means of communication. The problem area thus tackled relates in particular to the maintaining of the service and the autonomy of the antennas and not the optimization of the coverage of a monitoring zone as a function of diverse constraints on the sensors.
Concerning the problem area of the optimal positioning of monitoring sensors, U.S. Pat. No. 7,395,195 proposes a device allowing the representation of a network of devices, the allocation of calculation resources and the positioning of said sensors. U.S. Pat. No. 7,693,049 implements a stochastic optimization technique essentially focused on the conservation of energy resources.
In addition to the fact that the two aforementioned patent applications are aimed at only part of the wider problem that the present invention proposes to solve, they also exhibit limitations in relation to the optimization constraints that they can take into account. Generally, the known schemes implement conventional optimization schemes such as the gradient scheme. Such schemes exhibit the following drawbacks. They afford a solution to the global optimization problem only if the optimization constraints are modeled by differentiable functions, this representing a significant limitation that the present invention is aimed at removing. Moreover these schemes do not make it possible to avoid the phenomena of local minima or maxima which represent unsatisfactory solutions. It is also possible to use schemes based on a genetic algorithm, but these are not applicable to all cost functionals and do not guarantee convergence to a valid solution in all cases.